This document claims priority under 35 U.S.C. xc2xa7 119 to French Patent Application No. 99 08 417 filed on Jul. 1, 1999, the entire contents of which are hereby incorporated herein by reference.
1. Field of the Invention
The invention pertains to a procedure and equipment for carrying out a complex process which generally have a level of numerical command/control based on the so-called CIM (Computer Integrated Manufacturing) model.
2. Discussion of the Background
Such a procedure and equipment integrate traditional techniques which include: (1) the acquisition and the designation of numerical and symbolic data; (2) numerical computations; (3) signal processing and recognition of shapes; (4) artificial intelligence, especially of techniques for representation of indefinite, spatial, and temporal knowledge as rules of logic of first order predicates, as objects, and as reports, and associated reasoning techniques; (5) automatic control of continuous and/or discrete systems, in time and space; and (6) system control algorithms in real time.
In order to describe the state of a process and the evolution of the state of the process one calls on the following traditional definitions (1)-(12).
(1) A process is a system of transformation of an incoming flow and an outgoing flow of material, energy or information; in a system such as a blast furnace of a steel plant or a cement producing revolving oven, the process transforms the matter or energy.
(2) Such a system of transformation proceeds according to a large set of phenomena that are related to one another according to a common goal, which corresponds to the goals of production in an imperfect environment.
(3) The goal of production of the process can be expressed in terms of adherence to constraints which affect certain incoming flows and certain outgoing flows. These constraints can in turn be expressed in terms of specific aims or ranges of values. The aims pertaining to the incoming flows pertain for example to the position of the actuators of the process that the operator responsible for process behavior must respect, as, for example, in the case of a blast furnace, a minimum and maximum proportion of coal consumption, a minimum and maximum flow rate of oxygen, while the goals pertaining to the outgoing flows express some constraints pertaining to the output that the conduct operator must satisfy, which include for example a range of melt temperatures, a minimum daily flow rate of pig iron, a range of silicon content in the melt or the dross.
(4) The environment is called imperfect in the sense that it is defined in an imprecise, uncertain and incomplete way; this environment limits the production possibilities of the process.
(5) A model of behavior of a process is an organized set of knowledge, or a xe2x80x9cbodyxe2x80x9d of knowledge, which is used to predict the state of the system as a function of the value of recorded quantities of the system and, for example, values of parameter measurement of the model.
(6) A process is dynamic when the quantities which occur in its functioning model, like the state variables X, the input variables U, and the output variables Y, are related by temporal relationships.
(7) The behavior of one quantity can be defined by the relation between the value of its magnitude x and time t; it is then represented by x(t).
(8) According to the traditional rules defined by the entire set of the pairs of values (Xxe2x80x2(t), X(t)) relative to each recorded quantity, where Xxe2x80x2 designates the derivative with respect to time t of the quantity X; the process can then be described by a relationship of the type:
Xxe2x80x2(t)=f[xcex8,X(t), U(t)] and Y(t)=h[xcex8, X(t), U(t)]
Where xcex8 designates some parameters, t the time variable here continues, f [ ] and h [ ] some functions that describe the process.
(9) A trajectory of state of a process for a time interval [↑min, ↑max] is defined by a sequence of points (Xxe2x80x2(t), X(t)) in which the values of t are included in this range.
(10) A dynamic process is complex in one or the other of the following cases: absence of mathematical model of behavior or mathematical model of inoperative behavior; absence of a physical model of behavior, due to the inadequacy of the scientific knowledge, for example, or inoperative behavior physical model which does not yield any exploitable numerical calculation algorithm, for example: a non-reversible model, a model that cannot be calculated, a model that results in prohibitive calculation times with respect to the required response time, and a chaotic model.
In the case of complex dynamic processes one therefore will generally use symbolic models that resemble the entire set of the knowledge bits and the expertise obtained from observation of the behavior of the process. In a traditional manner one can construct such a model of behavior from knowledge possessed by experts during conduct of the process in question, by employing: logical formalisms that allow one to represent this knowledge; methodological tools that allow the acquisition of the knowledge for the purpose of their representation; and techniques for solving problems that have been posed to automatically find solutions to problems expressed according to these formalisms.
Among the formalisms employed one can mention: (1) the representation of the descriptive knowledge in the form of objects, classes, and meta-classes; (2) the representation of deductive knowledge by a logic of the first order predicates; (3) the representation of temporal knowledge in the form a reified temporal logic; and (4) the representation of spatial and temporal knowledge in the form of charts of discrete events.
Among the problem solving techniques we can mention: (1) reasoning through the memory of properties and behaviors; (2) reasoning based on the logic of the first order predicates, of the so-called xe2x80x9cModus Ponensxe2x80x9d type for example; (3) control of reasoning directed by the events, by the compilation of rules with trees of binary events for example; (4) management of time constraints; (5) signal processing by filtering, for example, by time and/or space segmentation, and by parametric identification; and (6) shape recognition, by multiple linear regression for example.
Among the methodological tools which allow the automatic exploitation of the knowledge we can mention system design methodologies with a knowledge base, such as the software product called xe2x80x9cOpenkads(trademark)xe2x80x9d or methodologies that work out the generic knowledge bases such as KADS.
The dynamic character of the behavior model is obtained by the use of formalisms that integrate the temporal constraints, by including concepts of reports and events for example to the logic of the first order predicates. One known example of formalism of this kind is called xe2x80x9cDEVSxe2x80x9d (Discrete Event System Specification), which allows one to define what is called discrete events from the inputs U, from the state X, from the outputs Y of internal transition functions Xxe2x86x92X, of the external transition functions Uxc3x97Xxe2x86x92X, of the output functions Xxc3x97Uxe2x86x92Y and of life duration functions of a state.
FIG. 1 shows such an abstraction with the discrete events E1 to E4 of an inputs U-outputs Y relationship.
In summary, the techniques for solving problems supply the technological and methodological tools that allow one to automate the exploitation of the knowledge that is used by experts to carry out a complex process and to translate these bits of knowledge into a behavioral model of the complex dynamic process.
As an example of a complex dynamic process we can mention: a blast furnace; an electric steel production oven; a glass production oven; a cement production oven; and, a rolled strip unit.
The present invention has a goal of providing a procedure and equipment capable of using this model of a complex dynamic process for the purpose of guiding the conduct of the process in conformity with the production goals of this process in an imperfect environment.
Fuzzy logic is a traditional means of using this kind of complex dynamic process model for the purpose of guiding the conduct of this process toward production goals, previously assigned or newly inserted. The fuzzy logic applied to this kind of model evaluates the level of adherence of the process state to one or several xe2x80x9csymbolic valuesxe2x80x9d of input of the model, deduces the xe2x80x9csymbolic valuesxe2x80x9d of corresponding outputs, and calculates the state of the system by weighting the output values as a function of the corresponding adherence levels.
Such a traditional means allows one to make the transitions between the different possible branches of reasoning on which the model is based more progressive.
But the disadvantage of such a means is that it leads one to neglect certain hypotheses of behavior of the process which can have, exceptionally or over the long term for example, very significant or catastrophic consequences, which may be very beneficial ones such that one thereby encounters phenomena that are rare but of exceptional importance.
Accordingly, one object of the present invention is to avoid such a disadvantage in the case of the use of complex dynamic process models.
In addition, from phenomena gathered on the basis of estimating the state of the complex dynamic process and its evolution in the course of time additional objectives are pursued.
One such additional objective is to give warning based on the xe2x80x9cproblematicxe2x80x9d nature of the current behavior of the process by explaining the problem encountered.
Another such additional objective is to evaluate the xe2x80x9cperformancexe2x80x9d of the process by reporting on the overall tendency toward improvement or degradation of the xe2x80x9coutputsxe2x80x9d of the process with respect to the goals being pursued.
Another such additional objective is to propose the commitment to correction actions for possible xe2x80x9cproblematicxe2x80x9d or inadequately performing behaviors; the action commitment corresponds for example to changing the rules for actuators and/or to modifications of the xe2x80x9cinputsxe2x80x9d of the process.
For these reasons, the present invention has as an object a procedure for controlling a process based on information issued by the environment of the process and in order to send the information to the operators of the process of such a kind to help them to maintain or to return the process to its production goals.
The present invention knowing how to determine and furnish only xe2x80x9ctimelyxe2x80x9d messages allows one to avoid information saturation of the operators and allows them to devote their attention to the most pertinent information at the opportune moment in order to affect the process control.
The evolution of the quantities is integrated in the definition base of each process-phenomenon which is used to describe the evolution of the process and its operational context. In a space, called the state space, whose dimensions correspond to these quantities, the overall evolution of the process or its overall functioning context, which result form the evolution of the quantities, can then be represented by a trajectory whose projection onto each dimension will correspond to the evolution of each quantity.
While the criteria affect the value of the quantities they also implicitly define a xe2x80x9ctypexe2x80x9d trajectory in the state space.
The invention can also present one or several of the following characteristics.
The description of the state of the evolution of the process and/or that of its functioning context includes reports of events, the definition of the events integrating that of one or several growth criteria of at least one recorded quantity and each event being assigned a date and/or a position, and each report being formed of a sequence of events ordered according to their date and/or their position. This additional characteristic completes the description of the state and of the process evolution and/or its context; in an advantageous way it can combine very different events and/or evolutions over time or at quite distant positions.
The previous definition of the process-phenomenon also integrates that of types of event reports.
This more complete definition allows one to identify developments over the long term and/or between very distant positions of the process which allow the operators of the process to control it by anticipating these developments. When used to describe the contexts it allows one to improve the judgment and to best select the information to send to the operators; the kinds of event reports are subsequently called xe2x80x9csignal-phenomenaxe2x80x9d.
The definition of the process-phenomenon and/or the events may also include that of one or several development criteria of the first derivative of at least one quantity with respect to a date and/or position variable, this derivative being a function of this variable.
If the evolution of the first derivative of the quantities is incorporated in the definition of certain process-phenomena and/or events according to the appropriate criteria for this additional characteristic of the invention, then one can represent the evolution of each quantity by a trajectory in a space that is appropriate for each quantity, in which the two dimensions correspond to this quantity and to its first derivative, and what is called the xe2x80x9cphase spacexe2x80x9d.
By means of the present invention the succession of detected process phenomena, which describes the state and development of the process and its functional context, can then include two representations: one in the form of a trajectory in the state space, and the other in the form of trajectories in the phase spaces.
One can therefore considerably improve the description of the state and evolution of the process because this more complete description of a process-phenomenon allows one, for example, to distinguish two phenomena that have the same trajectory in the state space but different trajectories in some phase spaces.
When used to describe the contexts, this additional characteristic of the invention also allows one to improve the judgment.
If the values of a quantity or, if necessary, of its first derivative are arranged according to the date and/or position variable to form a xe2x80x9csignalxe2x80x9d, for the development criteria which affect the parameters calculated from this signal, to be observed only in a predetermined range of values of the variable, called the xe2x80x9canalysis scalexe2x80x9d, the definition of each of the criteria is included in the classification of the calculated value of each parameter which the criterion will have in different predetermined sub-domains of the domain of values of this parameter, each sub-domain being defined by a lower limit and an upper limit.
If the parameter is the xe2x80x9cidentityxe2x80x9d function and the analysis scale pertains to only one variable value, this definition of the criteria corresponds to partitioning of the state space (lower limit and upper limit for each sub-domain of values of a quantity) and to partitioning of the phase spaces (lower limit and upper limit for each sub-domain of values of a quantity and for each sub-domain of values of the first derivative of a quantity).
At least some of the criteria can be determined as a function of the production goals of the process, and the production goals can be reevaluated at each acquisition period of the information and the criteria is updated as a consequence.
By means of the present invention according to which, during each acquisition cycle, one reevaluates the production goals by, for example, updating the xe2x80x9caimsxe2x80x9d for every recorded quantity then one updates the criteria which define the phenomena, the control procedure automatically adjusts to the changes of conditions or of operating context of the process and the evolution of the process itself, with respect to its wear and tear and/or its aging for example.
Thus, the control procedure according to the invention is xe2x80x9creactivexe2x80x9d in the sense that it is capable of reacting, in autonomous mode, to modifications of its environment.
In order to determine the criteria as a function of the production goals one can vary these limits as a function of these goals.
Finally, the present invention also has an objective of using the procedure according to the present invention to control a complex dynamic process, the process being chosen within the group that includes a blast furnace, a heat-treating furnace, such as a cement producing oven, a glass making oven or an iron slab oven, a unit with continuous metal flow, and a unit for rolled strip or continuous coating of a metal sheet.